Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

Xu Xinyi*

Lingjuan Lyu

Xingjun Ma*

Chenglin Miao*

Chuan-Sheng Foo*

Bryan Kian Hsiang Low*

* External authors

NeurIPS-2021

2021

Abstract

Collaborative machine learning provides a promising framework for different agents to pool their resources (e.g., data) for a common learning task. In realistic settings where agents are self-interested and not altruistic, they may be unwilling to share data or model without adequate rewards. Furthermore, as the data/model the agents share may differ in quality, designing rewards which are fair to them is important so they do not feel exploited and discouraged from sharing. In this paper, we investigate this problem in gradient-based collaborative machine learning. We propose a novel cosine gradient Shapley to evaluate the agents’ contributions and design commensurate rewards in the form of better models. Compared to existing baselines, our method is more efficient and does not require a validation dataset. We provide theoretical fairness guarantees and empirically validate the effectiveness of our method.